Regularized successive interference cancellation (SIC) under mismatched modeling

Authors

Jun Tong, University of WollongongFollow
Li Li, University of Queensland, Hubei University, Chinese Academy Of Sciences, Beijing, China, University of Queensland, University of Paderborn, Chinese Academy Of Sciences
Qinghua Guo, University of WollongongFollow
Peter J. Schreier, University of Paderborn
Jiangtao Xi, University of WollongongFollow

RIS ID

94341

Publication Details

J. Tong, L. Li, Q. Guo, P. J. Schreier & J. Xi, "Regularized successive interference cancellation (SIC) under mismatched modeling," in IEEE Workshop on Statistical Signal Processing Proceedings, 2014, pp. 328-331.

Abstract

Successive interference cancellation (SIC) has been extensively applied to estimate transmit signals in communication systems. When the channel state information (CSI) and noise statistics are imperfectly estimated, the standard SIC estimators that ignore the model mismatch may perform poorly. This paper introduces regularized SIC estimation to provide robustness against the model mismatch. Suboptimal, low-complexity implementations using (sorted) QR decomposition and approximate choice of regularization parameters are also introduced. Simulation examples demonstrate that the regularized SIC estimators can significantly outperform the standard version. 2014 IEEE.

Grant Number

ARC/DE120101266

Additional Grant Number

http://purl.org/au-research/grants/ARC/DE120101266